Can customers create the perfect product?

It’s obvious to say that customers have always had a role in influencing brand activity, whether through customer insight or crowdsourcing.

While this may sound sensible, do these approaches stumble? As Henry Ford is claimed to have said: “if I had asked my customers what they wanted, they’d have said a faster horse”. Whether the power is in the hands of the brand or given to customers, applying consumer feedback has always been an important part of innovation strategy.

As data capture has become more sophisticated and the communication between brand and customer is increasingly seamless and transparent, more companies are using this as an opportunity to give their customers exactly what they say they want. But to what extent should customers be directly involved in the process?

The four IntelligentX beers available as it launched

A new craft beer launched in London a few months ago, IntelligentX, which invites its customers to influence its next batch through an online chatbot. The chatbot’s artificially intelligent algorithm aggregates and evaluates feedback, and makes changes to its recipe based on the customer’s level of taste and connoisseurship of ales.

IntelligentX was created to challenge other beer brands, with the view of making the perfect product that truly responds to what customers really want and being able to apply changes quickly and transparently.

“I believe the future of innovation is about creating remarkable products and services people want and need. It’s about baking marketing into your product, not annoying people with ads until they buy it,” Hew Leith, CEO of 10x and co-creator of IntelligentX, tells GDR.

To prevent IntelligentX beers simply regressing to the mean and end up producing generic beers, its algorithm throws in “wildcard” ingredients and other random variables to keep the experience interesting.

“At the end of the day it’s about making a better product or service. This is why startups like KIND and Dollar Shave Club are doing well – they’re making better products that are more finely tuned to what their customers want. Because they’re more nimble, they can adapt to their community’s wants and needs where bigger corporations find it more difficult to do this as quickly as the startups nipping at their toes.”

Styr’s protein starter pack

Another approach to making the perfect product comes from startup STYR Labs: its mission is to bring customised nutrition to its customers. Through an arsenal of wearables, smart scales and an app, STYR Labs intends to give its customers personalised nutrition supplements based on a mountain of different data collected and synced from different digital touchpoints: fat percentage, muscle mass, dietary objections, calorie intake and fitness objectives to name a few. As your goals or metrics change, your future supplement plan will change with it without having to lift a finger.

Not only does STYR Labs’ network capture lots of intimate data, but it locks customers into its system by taking care of the other parts of the customer relationship. The personalised supplements come in individualised sachets that are home-delivered for maximum convenience. (By the way, the company has announced that it will be getting its own Amazon Dash button too.) What this brand does is revolve around its customer’s unique traits, but remain in control of its USP.

Provided that data capture is transparent and reliable, it won’t be long before more of these systems become the norm. But brands have to decide for themselves where it’s appropriate to involve customers in its production processes. One of the criticisms of mass-customisation is that failing to establish any design parameters and giving total free reign to customers is unsuccessful: customers are not always the experts in creating the product that is right for them.

Whatever the approach, it’s important to listen to your customers. A brand perhaps needs to decide whether to lead or follow to remain either idiosyncratic or more acceptable to the masses. Then they can decide what role data plays in the problem and how to gather it effectively.